class_docstr: Your Python model must use the same class name. Every Python model
  that is created must have "TritonPythonModel" as the class name.
docstr: `execute` must be implemented in every Python model. `execute`
  function receives a list of pb_utils.InferenceRequest as the only
  argument. This function is called when an inference is requested
  for this model. Depending on the batching configuration (e.g. Dynamic
  Batching) used, `requests` may contain multiple requests. Every
  Python model, must create one pb_utils.InferenceResponse for every
  pb_utils.InferenceRequest in `requests`. If there is an error, you can
  set the error argument when creating a pb_utils.InferenceResponse.
  Parameters
  ----------
  requests : list
    A list of pb_utils.InferenceRequest
  Returns
  -------
  list
    A list of pb_utils.InferenceResponse. The length of this list must
    be the same as `requests`
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code: def execute(self, requests):
      """`execute` must be implemented in every Python model. `execute`
      function receives a list of pb_utils.InferenceRequest as the only
      argument. This function is called when an inference is requested
      for this model. Depending on the batching configuration (e.g. Dynamic
      Batching) used, `requests` may contain multiple requests. Every
      Python model, must create one pb_utils.InferenceResponse for every
      pb_utils.InferenceRequest in `requests`. If there is an error, you can
      set the error argument when creating a pb_utils.InferenceResponse.
      Parameters
      ----------
      requests : list
        A list of pb_utils.InferenceRequest
      Returns
      -------
      list
        A list of pb_utils.InferenceResponse. The length of this list must
        be the same as `requests`
      """
      responses = []
      for request in requests:
          infer_outputs = pb_utils.get_input_tensor_by_name(
              request, self.input_names[0]
          )
          im_info = pb_utils.get_input_tensor_by_name(
              request, self.input_names[1]
          )
          infer_outputs = infer_outputs.as_numpy()
          im_info = im_info.as_numpy()
          for i in range(im_info.shape[0]):
              im_info[i] = json.loads(
                  im_info[i].decode("utf-8").replace("'", '"')
              )
  
          results = self.postprocess_.run([infer_outputs], im_info[0])
          r_str = fd.vision.utils.fd_result_to_json(results)
  
          r_np = np.array(r_str, dtype=np.object_)
          out_tensor = pb_utils.Tensor(self.output_names[0], r_np)
          inference_response = pb_utils.InferenceResponse(
              output_tensors=[
                  out_tensor,
              ]
          )
          responses.append(inference_response)
      return responses
  
file_path: PaddleSeg/deploy/fastdeploy/semantic_segmentation/serving/fastdeploy_serving/models/postprocess/1/model.py
imports: import json
  import numpy
  import time
  import os
  import fastdeploy
  import triton_python_backend_utils
label_desc: Use image node_id 2 for calling the TritonPythonModel obj's underlying member method code with example usage: obj.execute(requests) and returns: responses